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SOCNET  October 2017

SOCNET October 2017

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 30 Oct 2017 12:24:33 -0400

Content-Type:

MULTIPART/MIXED

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TEXT/PLAIN (209 lines)

*****  To join INSNA, visit http://www.insna.org  *****



   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
   https://urldefense.proofpoint.com/v2/url?u=http-3A__www.chass.utoronto.ca_-7Ewellman&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=Is-h95UALnx_JbnatrtFJLw4Gynx7NqMoRnEn5YJnbc&e=             https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=pn4GtHso2aX9Tt4tXRdaYnayvz-YKfgnbzoh5-_Toyg&e= 
   _______________________________________________________________________

for full stuff, go to their website/Barry
---------- Forwarded message ----------
Date: Mon, 30 Oct 2017 12:05:07 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dd4826fe172-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=oF5Nv9gHLJIRMAMaeWbwQyiwag9_wT0qAqu8vC6XMMg&e= 



Complex Contagions: A Decade in Review

    Since the publication of 'Complex Contagions and the Weakness of Long 
Ties' in 2007, complex contagions have been studied across an enormous 
variety of social domains. In reviewing this decade of research, we 
discuss recent advancements in applied studies of complex contagions, 
particularly in the domains of health, innovation diffusion, social media, 
and politics. We also discuss how these empirical studies have spurred 
complementary advancements in the theoretical modeling of contagions, 
which concern the effects of network topology on diffusion, as well as the 
effects of individual-level attributes and thresholds. In synthesizing 
these developments, we suggest three main directions for future research. 
The first concerns the study of how multiple contagions interact within 
the same network and across networks, in what may be called an ecology of 
contagions. The second concerns the study of how the structure of 
thresholds and their behavioral consequences can vary by individual and 
social context. The third area concerns the roles of diversity and 
homophily in the dynamics of complex contagion, including both diversity 
of demographic profiles among local peers, and the broader notion of 
structural diversity within a network. Throughout this discussion, we make 
an effort to highlight the theoretical and empirical opportunities that 
lie ahead.


Complex Contagions: A Decade in Review
Douglas Guilbeault, Joshua Becker, Damon Centola

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D5284650556-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=Bb5pqo7K5IX4PHMxlykuYp1OECkNWKam6Kb8M9eCUD0&e= )


Human Mobility: Models and Applications

    Recent years have witnessed an explosion of extensive geolocated 
datasets related to human movement, enabling scientists to quantitatively 
study individual and collective mobility patterns, and to generate models 
that can capture and reproduce the spatiotemporal structures and 
regularities in human trajectories. The study of human mobility is 
especially important for applications such as estimating migratory flows, 
traffic forecasting, urban planning, and epidemic modeling. In this 
survey, we review the approaches developed to reproduce various mobility 
patterns, with the main focus on recent developments. This review can be 
used both as an introduction to the fundamental modeling principles of 
human mobility, and as a collection of technical methods applicable to 
specific mobility-related problems. The review organizes the subject by 
differentiating between individual and population mobility and also 
between short-range and long-range mobility. Throughout the text the 
description of the theory is intertwined with real-world applications.


Human Mobility: Models and Applications Hugo Barbosa-Filho, Marc 
Barthelemy, Gourab Ghoshal, Charlotte R. James, Maxime Lenormand, Thomas 
Louail, Ronaldo Menezes, José J. Ramasco, Filippo Simini, Marcello 
Tomasini

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3De6301bbe64-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=P94iNLU_xXmPQOCThre8uoZSnuUP5unhKRoQ8UnFgJc&e= )



Predicting stock market movements using network science: an information theoretic approach

    A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method to forecast the future movements of Standard & Poor˙˙s 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-min price movements of the pairs of the companies with the consecutive 5340 min price records. We showed that the changes in the strength distributions of the networks provide an important information on the network˙˙s future movements. We built several metrics using the strength distributions and network measurements such as centrality, and we combined the best two predictors by performing a linear combination. We found that the
combined predictor and the changes in S&P 500 show a quadratic relationship, and it allows us to predict the amplitude of the one step future change in S&P 500. The result showed significant fluctuations in S&P 500 Index when the combined predictor was high. In terms of making the actual index predictions, we built ARIMA models with and without inclusion of network measurements, and compared the predictive power of them. We found that adding the network measurements into the ARIMA models improves the model accuracy. These findings are useful for financial market policy makers as an indicator based on which they can interfere with the markets before the markets make a drastic change, and for quantitative investors to improve their forecasting models.


Predicting stock market movements using network science: an information theoretic approach
Minjun Kim and Hiroki Sayama
Applied Network Science 2017 2:35
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Debdcd73f68-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=K3vfLsk0qowLraf61vvb_ItwaCZQ4tQ4v3pKqvdbLr4&e= 

Source: appliednetsci.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D62945cfa8b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=OU_Q3HIzWWt2Fw83l0jc36PK0NsWmPu2c_0ac0IRShw&e= )



Universal Scaling in Complex Substitutive Systems

    Diffusion processes are central to human interactions. Despite extensive studies that span multiple disciplines, our knowledge is limited to spreading processes in non-substitutive systems. Yet, a considerable number of ideas, products and behaviors spread by substitution-to adopt a new one, agents must give up an existing one. Here, we find that, ranging from mobile handsets to automobiles to smart phone apps, early growth patterns in substitutive systems follow a power law with non-integer exponents, in sharp contrast to the exponential growth customary in spreading phenomena. Tracing 3.6 million individuals substituting for mobile handsets for over a decade, we uncover three generic ingredients governing substitutive processes, allowing us to develop a minimal substitution model, which not only predict analytically the observed growth patterns, but also collapse growth trajectories of constituents from rather diverse systems into a single universal curve. These results
demonstrate that the dynamics of complex substitutive systems are governed by robust self-organizing principles that go beyond the particulars of individual systems, which implies that these results could guide the understanding and prediction of all spreading phenomena driven by substitutions, from electric cars to scientific paradigms, from renewable energy to new healthy habits.


Universal Scaling in Complex Substitutive Systems
Ching Jin, Chaoming Song, Johannes Bjelland, Geoffrey Canright, Dashun Wang

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D25868b6a2b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=qp4M-ZW-s2Tyf_9eK16lGMRME2RK3_oHcCEXwCUrUrw&e= )



Computational Social Scientist Beware: Simpson˙˙s Paradox in Behavioral Data

    Observational data about human behavior is often heterogeneous, i.e., 
generated by subgroups within the population under study that vary in size 
and behavior. Heterogeneity predisposes analysis to Simpson's paradox, 
whereby the trends observed in data that has been aggregated over the 
entire population may be substantially different from those of the 
underlying subgroups. I illustrate Simpson's paradox with several examples 
coming from studies of online behavior and show that aggregate response 
leads to wrong conclusions about the underlying individual behavior. I 
then present a simple method to test whether Simpson's paradox is 
affecting results of analysis. The presence of Simpson's paradox in social 
data suggests that important behavioral differences exist within the 
population, and failure to take these differences into account can distort 
the studies' findings.


Computational Social Scientist Beware: Simpson's Paradox in Behavioral Data
Kristina Lerman

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D84371b4b0b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=SacvwUiz1Hh-WXZsKqEAIWRAbL3eO3uURwJsSACUx_8&e= )



Generating realistic scaled complex networks

    Research on generative models plays a central role in the emerging 
field of network science, studying how statistical patterns found in real 
networks could be generated by formal rules. Output from these generative 
models is then the basis for designing and evaluating computational 
methods on networks including verification and simulation studies. During 
the last two decades, a variety of models has been proposed with an 
ultimate goal of achieving comprehensive realism for the generated 
networks. In this study, we (a) introduce a new generator, termed ReCoN; 
(b) explore how ReCoN and some existing models can be fitted to an 
original network to produce a structurally similar replica, (c) use ReCoN 
to produce networks much larger than the original exemplar, and finally 
(d) discuss open problems and promising research directions. In a 
comparative experimental study, we find that ReCoN is often superior to 
many other state-of-the-art network generation methods. We argue that 
ReCoN is a scalable and effective tool for modeling a given network while 
preserving important properties at both micro- and macroscopic scales, and 
for scaling the exemplar data by orders of magnitude in size.


Generating realistic scaled complex networks
Christian L. Staudt, Michael Hamann, Alexander Gutfraind, Ilya Safroand Henning Meyerhenke
Applied Network Science 2017 2:36
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dcc4235a589-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=26iWZj-JpJRycUScABsdbB27sWIY9DxaVwGUvHTfW5w&e= 

Source: appliednetsci.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D497096ef12-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=Qrb1jU17L_Au5DFuceOPXNzwY_QNk9WGWhCBSnIVxDo&e= )


Interdisciplinary Training in Complex Networks and Systems

    Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how dynamic brain connections give rise to thoughts and behaviors, to detecting and preventing the spread of misinformation or unhealthy behaviors across a population. Graduate training, however, typically occurs in one of two dimensions: experimental and observational methods in a specific area such as biology and sociology, or in general methodologies such as machine learning and data science.

With more and more students seeking to gain sufficient expertise in mathematical and computational methods on top of domain-specific laboratory and social analysis methodologies, a greater demand for more efficient training is emerging. This National Science Foundation Research Traineeship (NRT) award to Indiana University will address this growing need with an integrated dual PhD program that trains students to be "bidisciplinary" in Complex Networks and Systems (CNS) and another discipline of their choosing from the natural and social sciences. It will seamlessly integrate traditional education with interdisciplinary hands-on research in a culture of academic and human diversity.


Applications Due December 1

Source: cns-nrt.indiana.edu (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da5ad46f6bf-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=3EIc9cVVOP0sjbedYmNkAcb8HwHAzJwUpTfBj71Ls1E&e= )



Complex Networks: Theory, Methods, and Applications | Lake Como School of Advanced Studies

    Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline---including sociology, transportation, economics and finance, biology, and myriad others---and the study of "network science" has thus become a crucial component of modern scientific education.

The school "Complex Networks: Theory, Methods, and Applications" offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.


Spring School
COMPLEX NETWORKS: THEORY, METHODS, AND APPLICATIONS
(4th edition)
Lake Como School of Advanced Studies
Villa del Grumello, Como, Italy, 14-18 May 2018

BW: Just back from Lake Como (altho not here): one of the most gorgeous 
places in the world


*** DEADLINE FOR APPLICATION: February 18, 2018 ***

Source: ntmd.lakecomoschool.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D4aa7b58219-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=cgz3EuAMXd2GUI6eurKs1Bol411_IkqUI-agc2L04ZY&s=3xVyyIUYGqJSr2-EPxs-gA6ya-v4pxk-mAxZ4BjJExE&e= )

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